Deep Learning-based Beam Tracking for Millimeter-wave Communications under Mobility

TL;DR

This paper introduces a deep learning-based beam tracking method using LSTM networks for mmWave communications, effectively predicting channel variations due to user mobility and improving link reliability.

Contribution

It presents a novel LSTM-based model that captures temporal channel behavior and integrates it into beam tracking, outperforming traditional methods in mobility scenarios.

Findings

Significant performance improvements over conventional beam tracking methods.

Effective prediction of future channel states using LSTM models.

Enhanced beam control and channel estimation accuracy under mobility.

Abstract

In this paper, we propose a deep learning-based beam tracking method for millimeter-wave (mmWave)communications. Beam tracking is employed for transmitting the known symbols using the sounding beams and tracking time-varying channels to maintain a reliable communication link. When the pose of a user equipment (UE) device varies rapidly, the mmWave channels also tend to vary fast, which hinders seamless communication. Thus, models that can capture temporal behavior of mmWave channels caused by the motion of the device are required, to cope with this problem. Accordingly, we employa deep neural network to analyze the temporal structure and patterns underlying in the time-varying channels and the signals acquired by inertial sensors. We propose a model based on long short termmemory (LSTM) that predicts the distribution of the future channel behavior based on a sequence of input signals available at the UE. This channel distribution is used to 1) control the sounding beams adaptively for the future channel state and 2) update the channel estimate through the measurement update step under a sequential Bayesian estimation framework. Our experimental results demonstrate that the proposed method achieves a significant performance gain over the conventional beam tracking methods under various mobility scenarios.